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It looks easy! Heuristics for combinatorial optimization problems.

Edward P Chronicle1, James N MacGregor, Thomas C Ormerod

  • 1Department of Psychology, University of Hawaii at Manoa, Honolulu, HI 96822, USA. chronicl@hawaii.edu

Quarterly Journal of Experimental Psychology (2006)
|May 19, 2006
PubMed
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Humans excel at complex optimization tasks like the traveling salesperson problem (TSP) using a simple boundary-following heuristic. This strategy leads to near-optimal solutions, outperforming methods that restrict its use.

Area of Science:

  • Cognitive psychology
  • Computational complexity
  • Artificial intelligence

Background:

  • Human performance on computationally intractable problems, such as the traveling salesperson problem (TSP), is often surprisingly high.
  • Existing models struggle to fully explain this human capability in complex optimization scenarios.

Purpose of the Study:

  • To investigate the role of a proposed boundary-following heuristic in human problem-solving for the traveling salesperson problem (TSP).
  • To experimentally validate the effectiveness of this heuristic and compare it against restricted conditions.

Main Methods:

  • Three experiments were conducted involving participants solving TSP instances.
  • The capacity to utilize a boundary-following heuristic was manipulated across experimental conditions.

Related Experiment Videos

  • Performance was measured by solution proximity to the optimal solution and compared against a computational model.

    • Main Results:

    • Participants permitted to use the boundary-following heuristic achieved significantly better TSP solutions compared to those restricted from its use.
    • This finding was replicated with larger problem sizes, and a potential confound was ruled out.
    • A boundary-following computational model closely matched human performance across all experiments.

    Conclusions:

    • Human performance on certain combinatorial optimization tasks, like the TSP, is effectively explained by simple, perceptually-based, boundary-following heuristics.
    • These findings suggest that humans may employ global processing strategies rather than purely local ones for these complex problems.